Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction and Environment Setup
- Understanding AutoML and its significance
- Establishing Python and R environments
- Configuring remote desktop and cloud environments
Exploring AutoML Capabilities
- Core functionalities of AutoML frameworks
- Hyperparameter optimization and search strategies
- Interpreting AutoML outputs and logs
Algorithm Selection in AutoML
- Gradient Boosting Machines (GBMs), Random Forests, GLMs
- Neural networks and deep learning backends
- Trade-offs: accuracy vs. interpretability vs. cost
Data Preparation and Preprocessing
- Working with numeric and categorical data
- Feature engineering and encoding strategies
- Handling missing values and data imbalance
AutoML for Various Data Types
- Tabular data (H2O AutoML, auto-sklearn, TPOT)
- Time-series data (forecasting and sequential modeling)
- Text and NLP tasks (classification, sentiment analysis)
- Image classification and computer vision (Auto-Keras, TensorFlow, PyTorch)
Model Deployment and Monitoring
- Exporting and deploying AutoML models
- Building pipelines for real-time prediction
- Monitoring model drift and retraining strategies
Ensembling and Advanced Topics
- Stacking and blending AutoML models
- Privacy and compliance considerations
- Cost optimization for large-scale AutoML
Troubleshooting and Case Studies
- Common errors and how to fix them
- Interpreting AutoML model performance
- Case studies from industry use cases
Summary and Next Steps
Requirements
- Familiarity with machine learning algorithms
- Proficiency in Python or R programming
Target Audience
- Data analysts
- Data scientists
- Data engineers
- Developers
14 Hours
